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Scientific Visualization

Scientific visualization, sometimes referred to as visual data analysis, uses the graphical representation of data as a means of gaining understanding and insight into the data. Scientific visualization research at SCI has focused on applications spanning computational fluid dynamics, medical imaging and analysis, and fire simulations. Research involves novel algorithm development to building tools and systems that assist in the comprehension of massive amounts of scientific data. In helping researchers to comprehend spatial and temporal relationships between data, interactive techniques provide better cues than noninteractive techniques; therefore, much of scientific visualization research focuses on better methods for visualization and rendering at interactive rates.

Visualization Project Sites:

Uncertainty Visualization
The graphical depiction of uncertainty information is emerging as a problem of great importance in the field of visualization. Scientific data sets are not considered complete without indications of error, accuracy, or levels of confidence, and this information is often presented as charts and tables alongside visual representations of the data. Uncertainty measures are often excluded from explicit representation within data visualizations because the increased visual complexity incurred can cause clutter, obscure the data display, and may lead to erroneous conclusions or false predictions. However, uncertainty is an essential component of the data, and its display must be integrated in order for a visualization to be considered a true representation of the data. The growing need for the addition of qualitative information into the visual representation of data, and the challenges associated with that need, command fundamental research on the visualization of uncertainty.

Projects
shape ProbVis: Interactive visualization of probability distribution functions.
The ProbVis software tool allows for the interactive display and exploration of a spatial collection of data distributions. A global display shows the value of a difference measure across the spatial domain. The user can change the measure from the L1 Norm to the Hellinger distance. The user is also given a pointer to explore the individual distributions
which are diplayed as a PDF or CDF in the lower corner. 
Interactive Visualization of Probability and Cumulative Density Functions
Kristin Potter, Robert M. Kirby, Dongbin Xiu, & Chris R. Johnson
International Journal for Uncertainty Quantification, to appear. 2011.


Meeting Schedule 
Meetings are held every other Tuesday at 2pm in the Jones conference room unless otherwise noted.
Spring 2011
Date Speaker Topic Note
1/11 Jeff Phillips There is uncertainty in your uncertainty
1/25 Fangxiang Jiao Review of uncertainty analysis and uncertainty visualization in Diffusion Tensor Imaging (DTI)
2/08 Liang Zhou Transfer function combinations
2/22 Shreeraj Jadhav Uncertain 2D Vector Field Topology
3/08 Mike Kirby Overview of the stochastic Galerkin and stochastic collocation methods
4/05 Kristi Potter A prototype for Material Models
4/19 Tobias Martin, Guoning Chen, and  Suraj Musuvathy Extraction and Harmonic Parameterization of Topology-Consistent Midstructures
5/03 Josh Levine



People

Faculty Research Staff Graduate Students
Chris R. Johnson  Yarden Livnant Fangxiang Jiao
Valerio Pascucci Kristin Potter Samuel Gerber
Mike Kirby Joel Daniels
Paul Rosen Jeff Phillips







Links

Uncertainty Visualization Reference Library

Publications

H. Bhatia, S. Jadhav, P.-T. Bremer, G. Chen, J.A. Levine, L.G. Nonato, V. Pascucci. “Edge Maps: Representing Flow with Bounded Error,” InProceedings of IEEE Pacific Visualization Symposium 2011, Hong Kong, China, pp. (accepted). March, 2011.

K. Potter, J.M. Kniss, R. Riesenfeld, C.R. Johnson. “Visualizing Summary Statistics and Uncertainty,” In Computer Graphics Forum (Proceedings of Eurovis 2010), Vol. 29, No. 3, pp. 823--831. 2010.

K. Potter, A. Wilson, P.-T. Bremer, D. Williams, C. Doutriaux, V. Pascucci, C.R. Johhson. “Visualization of Uncertainty and Ensemble Data: Exploration of Climate Modeling and Weather Forecast Data with Integrated ViSUS-CDAT Systems,” In Proceedings of SciDAC 2009, Journal of Physics: Conference Series, Vol. 180, No. 012089, pp. (published online). 2009.

K. Potter, A. Wilson, P.-T. Bremer, D. Williams, C. Doutriaux, V. Pascucci, C.R. Johnson. “Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data,” In Proceedings of the 2009 IEEE International Conference on Data Mining Workshops, pp. 233--240. 2009.

K. Potter, J. Krueger, C.R. Johnson. “Towards the Visualization of Multi-Dimentional Stochastic Distribution Data,” In Proceedings of The International Conference on Computer Graphics and Visualization (IADIS) 2008, pp. 191--196. 2008.

J.M. Kniss, R. Van Uitert, A.J. Stephens, G. Li, T. Tasdizen. “Statistically Quantitative Volume Visualization,” In IEEE Visualization 2005, 2005.


C.R. Johnson. “Top Scientific Visualization Research Problems,” In IEEE Computer Graphics and Applications: Visualization Viewpoints, Vol. 24, No. 4, pp. 13--17. July/August, 2004.

G. Kindlmann, R.T. Whitaker, T. Tasdizen, T. Möller. “Curvature-Based Transfer Functions for Direct Volume Rendering: Methods and Applications,” In Proceedings Visualization 2003, pp. 67. October, 2003.


C.R. Johnson, A.R. Sanderson. “A Next Step: Visualizing Errors and Uncertainty,” In IEEE Computer Graphics and Applications, Vol. 23, No. 5, Edited by Theresa-Marie Rhyne, pp. 6--10. September/October, 2003.